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# Senior AI/ML Engineer

- **Company:** Fmr LLC
- **Location:** Westlake, TX, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Data Analysis, Automated Storage and Retrieval Systems, Encodings, Continuous Integration, Data Integrity, Extract Transform Load (ETL), Database Queries, Software Debugging, Distributed Systems, Identity and Access Management, Python (Programming Language), Open Source Technology, Operational Databases, Tensorflow, Runbook, Data Streaming, Datadog, Large Language Models, Snowflake, Multi-Agent Systems, Apache Spark, State Machines, Llamaindex, Parallel Computation, Indexer, Agentic-AI, Cloudformation, Event Driven Architecture, Build Management, Amazon Relational Database Service, Containerization, Git Flow, Kubernetes, Apache Kafka, Machine Learning Operations, Functional Programming, Drift Detection, Cloudwatch, Terraform, GPT, Data Pipelines, Docker
- **Published:** October 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c90944751a08861f

## About the Role

* Contribute to operational readiness through runbooks, incident response, and production support
 * Strong Python skills for production data and AI/ML systems, including rigorous testing and maintainable code
 * Strong SQL skills and experience with Snowflake for analytics and data pipeline workloads
 * Experience with AWS services (EC2, S3, Lambda, Step Functions, SageMaker, IAM, RDS)
 * Experience building and operating data pipelines using Spark, Airflow, or streaming frameworks (Kafka, Kinesis)
 * Experience deploying and monitoring AI/ML/LLM services in containerized environments (Docker, Kubernetes)
 * Experience with CI/CD and Infrastructure as Code (Terraform, CloudFormation, or similar)

 * Comfort debugging complex distributed systems with evidence-driven approaches
 * Experience with vector databases and retrieval-augmented generation (RAG) patterns
 * Familiarity with LLM frameworks (LangChain, LlamaIndex, or similar) and agent orchestration
 * Experience with observability tooling such as Datadog or CloudWatch
 * Familiarity with event-driven architectures and real-time streaming systems
 * Experience designing developer-facing APIs, SDKs, or CLI tools
 * Understanding of agentic AI patterns and multi-step reasoning workflows

## Description

We are looking for a Senior Engineer who operates at the intersection of data and AI/ML/LLM Engineering. You will work on the end-to-end lifecycle from building robust data pipelines that feed AI systems, to deploying and operating AI/LLM and retrieval systems at scale. This is a hands-on engineering role, not data science.

 * Real-time and batch data pipeline engineering on AWS and Snowflake
 * Production-grade Python, testing, and CI/CD discipline
 * AWS and Snowflake-based data and deployment patterns
 * Productionizing AI/ML/LLM systems (serving, agents, retrieval pipelines)
 * Observability, monitoring, and operational reliability of AI-powered systems

The Skills and Expertise You Bring

 * Primarily hands-on, with the ability to raise technical standards through direct execution and engineering excellence.
 * Design and build real-time and batch data pipelines: ingestion, transformation, streaming, and delivery into analytics and AI/ML systems
 * Build resilient data connectors, chunking/embedding workflows, and parallel processing frameworks with data integrity guarantees
 * Optimize ETL/ELT workflows in AWS and Snowflake including indexing, partitioning and efficient resource utilization
 * Deploy, scale, and operate AI/ML/LLM services including model serving, agent orchestration, and retrieval pipelines
 * Implement monitoring and observability for production AI systems for retrieval quality metrics, latency tracking, drift detection, and alerting
 * Translate prototypes into production-grade systems that are pragmatic, hardware-efficient, and simple to operate
 * Evaluate open-source AI/ML frameworks critically; adopt what's production-ready, replace bloat with lean, maintainable implementations
 * Develop production-grade Python code with strong testing, clean interfaces, and maintainability practices
 * Work within Git-based workflows and CI/CD pipelines to support reliable promotion across dev, UAT, and production environments